



'''simple docstring'''





import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
lowercase_									=    sys.version_info >= (3, 10)




def lowerCAmelCase    (__A=None    ,      __A=None):



     """simple docstring"""



     return field(default_factory=lambda: default    ,      metadata=__A)






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			int
 __lowerCamelCase       :			float
 __lowerCamelCase       :			str
 __lowerCamelCase       :			bool






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			int		=			42
 __lowerCamelCase       :			str		=			field(default='toto'  ,  metadata={'help': 'help message'}		)






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			bool		=			False
 __lowerCamelCase       :			bool		=			True
 __lowerCamelCase       :			Optional[bool]		=			None






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			'titi'
 __lowerCamelCase       :			Union[str, Any]		=			'toto'






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Tuple		=			'titi'
 __lowerCamelCase       :			Union[str, Any]		=			'toto'
 __lowerCamelCase       :			Optional[int]		=			42






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			BasicEnum		=			"toto"



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    BasicEnum(self.foo			)






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			MixedTypeEnum		=			"toto"



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    MixedTypeEnum(self.foo			)






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			None
 __lowerCamelCase       :			Optional[float]		=			field(default=A  ,  metadata={'help': 'help message'}		)
 __lowerCamelCase       :			Optional[str]		=			None
 __lowerCamelCase       :			Optional[List[str]]		=			list_field(default=[]		)
 __lowerCamelCase       :			Optional[List[int]]		=			list_field(default=[]		)






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			List[int]		=			list_field(default=[]		)
 __lowerCamelCase       :			List[int]		=			list_field(default=[1, 2, 3]		)
 __lowerCamelCase       :			List[str]		=			list_field(default=['Hallo', 'Bonjour', 'Hello']		)
 __lowerCamelCase       :			List[float]		=			list_field(default=[0.1, 0.2, 0.3]		)






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			List[int]		=			field()
 __lowerCamelCase       :			str		=			field()
 __lowerCamelCase       :			BasicEnum		=			field()



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    BasicEnum(self.required_enum			)






@dataclass
class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			int
 __lowerCamelCase       :			"BasicEnum"		=			field()
 __lowerCamelCase       :			"Optional[bool]"		=			None
 __lowerCamelCase       :			"str"		=			field(default='toto'  ,  metadata={'help': 'help message'}		)
 __lowerCamelCase       :			"List[str]"		=			list_field(default=['Hallo', 'Bonjour', 'Hello']		)


if is_python_no_less_than_3_10:






  @dataclass
  class __A     :



   '''simple docstring'''







   __lowerCamelCase       :			bool		=			False
   __lowerCamelCase       :			bool		=			True
   __lowerCamelCase       :			bool | None		=			None






  @dataclass
  class __A     :



   '''simple docstring'''







   __lowerCamelCase       :			int | None		=			None
   __lowerCamelCase       :			float | None		=			field(default=A  ,  metadata={'help': 'help message'}		)
   __lowerCamelCase       :			str | None		=			None
   __lowerCamelCase       :			list[str] | None		=			list_field(default=[]		)
   __lowerCamelCase       :			list[int] | None		=			list_field(default=[]		)






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def    a__    (self					,  A					,  A			)       ->	List[Any]:


      """simple docstring"""



      self.assertEqual(len(a._actions			)					,  len(b._actions			)			)
      for x, y in zip(a._actions					,  b._actions			):
           _a           =    {k: v for k, v in vars(A			).items() if k != '''container'''}
           _a           =    {k: v for k, v in vars(A			).items() if k != '''container'''}

           # Choices with mixed type have custom function as "type"
           # So we need to compare results directly for equality
           if xx.get('''choices'''					,  A			) and yy.get('''choices'''					,  A			):
                for expected_choice in yy["choices"] + xx["choices"]:
                     self.assertEqual(xx['''type'''](A			)					,  yy['''type'''](A			)			)
                del xx["type"], yy["type"]

           self.assertEqual(A					,  A			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    argparse.ArgumentParser()
      expected.add_argument('''--foo'''					,  type=A					,  required=A			)
      expected.add_argument('''--bar'''					,  type=A					,  required=A			)
      expected.add_argument('''--baz'''					,  type=A					,  required=A			)
      expected.add_argument('''--flag'''					,  type=A					,  default=A					,  const=A					,  nargs='''?'''			)
      self.argparsersEqual(A					,  A			)

      _a           =    ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
      ((_a)    ,       )           =    parser.parse_args_into_dataclasses(A					,  look_for_args_file=A			)
      self.assertFalse(example.flag			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    argparse.ArgumentParser()
      expected.add_argument('''--foo'''					,  default=42					,  type=A			)
      expected.add_argument('''--baz'''					,  default='''toto'''					,  type=A					,  help='''help message'''			)
      self.argparsersEqual(A					,  A			)



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    argparse.ArgumentParser()
      expected.add_argument('''--foo'''					,  type=A					,  default=A					,  const=A					,  nargs='''?'''			)
      expected.add_argument('''--baz'''					,  type=A					,  default=A					,  const=A					,  nargs='''?'''			)
      # A boolean no_* argument always has to come after its "default: True" regular counter-part
      # and its default must be set to False
      expected.add_argument('''--no_baz'''					,  action='''store_false'''					,  default=A					,  dest='''baz'''			)
      expected.add_argument('''--opt'''					,  type=A					,  default=A			)

      _a           =    [WithDefaultBoolExample]
      if is_python_no_less_than_3_10:
           dataclass_types.append(A			)

      for dataclass_type in dataclass_types:
           _a           =    HfArgumentParser(A			)
           self.argparsersEqual(A					,  A			)

           _a           =    parser.parse_args([]			)
           self.assertEqual(A					,  Namespace(foo=A					,  baz=A					,  opt=A			)			)

           _a           =    parser.parse_args(['''--foo''', '''--no_baz''']			)
           self.assertEqual(A					,  Namespace(foo=A					,  baz=A					,  opt=A			)			)

           _a           =    parser.parse_args(['''--foo''', '''--baz''']			)
           self.assertEqual(A					,  Namespace(foo=A					,  baz=A					,  opt=A			)			)

           _a           =    parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True''']			)
           self.assertEqual(A					,  Namespace(foo=A					,  baz=A					,  opt=A			)			)

           _a           =    parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False''']			)
           self.assertEqual(A					,  Namespace(foo=A					,  baz=A					,  opt=A			)			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    argparse.ArgumentParser()
      expected.add_argument(
          '''--foo'''					,  default='''toto'''					,  choices=['''titi''', '''toto''', 42]					,  type=make_choice_type_function(['''titi''', '''toto''', 42]			)					,  )
      self.argparsersEqual(A					,  A			)

      _a           =    parser.parse_args([]			)
      self.assertEqual(args.foo					,  '''toto'''			)
      _a           =    parser.parse_args_into_dataclasses([]			)[0]
      self.assertEqual(enum_ex.foo					,  MixedTypeEnum.toto			)

      _a           =    parser.parse_args(['''--foo''', '''titi''']			)
      self.assertEqual(args.foo					,  '''titi'''			)
      _a           =    parser.parse_args_into_dataclasses(['''--foo''', '''titi''']			)[0]
      self.assertEqual(enum_ex.foo					,  MixedTypeEnum.titi			)

      _a           =    parser.parse_args(['''--foo''', '''42''']			)
      self.assertEqual(args.foo					,  42			)
      _a           =    parser.parse_args_into_dataclasses(['''--foo''', '''42''']			)[0]
      self.assertEqual(enum_ex.foo					,  MixedTypeEnum.fourtytwo			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      @dataclass
      class __A     :



       '''simple docstring'''







       __lowerCamelCase       :			Literal["titi", "toto", 42]		=			"toto"





      _a           =    HfArgumentParser(A			)

      _a           =    argparse.ArgumentParser()
      expected.add_argument(
          '''--foo'''					,  default='''toto'''					,  choices=('''titi''', '''toto''', 42)					,  type=make_choice_type_function(['''titi''', '''toto''', 42]			)					,  )
      self.argparsersEqual(A					,  A			)

      _a           =    parser.parse_args([]			)
      self.assertEqual(args.foo					,  '''toto'''			)

      _a           =    parser.parse_args(['''--foo''', '''titi''']			)
      self.assertEqual(args.foo					,  '''titi'''			)

      _a           =    parser.parse_args(['''--foo''', '''42''']			)
      self.assertEqual(args.foo					,  42			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    argparse.ArgumentParser()
      expected.add_argument('''--foo_int'''					,  nargs='''+'''					,  default=[]					,  type=A			)
      expected.add_argument('''--bar_int'''					,  nargs='''+'''					,  default=[1, 2, 3]					,  type=A			)
      expected.add_argument('''--foo_str'''					,  nargs='''+'''					,  default=['''Hallo''', '''Bonjour''', '''Hello''']					,  type=A			)
      expected.add_argument('''--foo_float'''					,  nargs='''+'''					,  default=[0.1, 0.2, 0.3]					,  type=A			)

      self.argparsersEqual(A					,  A			)

      _a           =    parser.parse_args([]			)
      self.assertEqual(
          A					,  Namespace(foo_int=[]					,  bar_int=[1, 2, 3]					,  foo_str=['''Hallo''', '''Bonjour''', '''Hello''']					,  foo_float=[0.1, 0.2, 0.3]			)					,  )

      _a           =    parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split()			)
      self.assertEqual(A					,  Namespace(foo_int=[1]					,  bar_int=[2, 3]					,  foo_str=['''a''', '''b''', '''c''']					,  foo_float=[0.1, 0.7]			)			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    argparse.ArgumentParser()
      expected.add_argument('''--foo'''					,  default=A					,  type=A			)
      expected.add_argument('''--bar'''					,  default=A					,  type=A					,  help='''help message'''			)
      expected.add_argument('''--baz'''					,  default=A					,  type=A			)
      expected.add_argument('''--ces'''					,  nargs='''+'''					,  default=[]					,  type=A			)
      expected.add_argument('''--des'''					,  nargs='''+'''					,  default=[]					,  type=A			)

      _a           =    [OptionalExample]
      if is_python_no_less_than_3_10:
           dataclass_types.append(A			)

      for dataclass_type in dataclass_types:
           _a           =    HfArgumentParser(A			)

           self.argparsersEqual(A					,  A			)

           _a           =    parser.parse_args([]			)
           self.assertEqual(A					,  Namespace(foo=A					,  bar=A					,  baz=A					,  ces=[]					,  des=[]			)			)

           _a           =    parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split()			)
           self.assertEqual(A					,  Namespace(foo=12					,  bar=3.14					,  baz='''42'''					,  ces=['''a''', '''b''', '''c''']					,  des=[1, 2, 3]			)			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    argparse.ArgumentParser()
      expected.add_argument('''--required_list'''					,  nargs='''+'''					,  type=A					,  required=A			)
      expected.add_argument('''--required_str'''					,  type=A					,  required=A			)
      expected.add_argument(
          '''--required_enum'''					,  type=make_choice_type_function(['''titi''', '''toto''']			)					,  choices=['''titi''', '''toto''']					,  required=A					,  )
      self.argparsersEqual(A					,  A			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    argparse.ArgumentParser()
      expected.add_argument('''--foo'''					,  type=A					,  required=A			)
      expected.add_argument(
          '''--required_enum'''					,  type=make_choice_type_function(['''titi''', '''toto''']			)					,  choices=['''titi''', '''toto''']					,  required=A					,  )
      expected.add_argument('''--opt'''					,  type=A					,  default=A			)
      expected.add_argument('''--baz'''					,  default='''toto'''					,  type=A					,  help='''help message'''			)
      expected.add_argument('''--foo_str'''					,  nargs='''+'''					,  default=['''Hallo''', '''Bonjour''', '''Hello''']					,  type=A			)
      self.argparsersEqual(A					,  A			)



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    {
          '''foo''': 12,
          '''bar''': 3.14,
          '''baz''': '''42''',
          '''flag''': True,
      }

      _a           =    parser.parse_dict(A			)[0]
      _a           =    BasicExample(**A			)
      self.assertEqual(A					,  A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    {
          '''foo''': 12,
          '''bar''': 3.14,
          '''baz''': '''42''',
          '''flag''': True,
          '''extra''': 42,
      }

      self.assertRaises(A					,  parser.parse_dict					,  A					,  allow_extra_keys=A			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    {
          '''foo''': 12,
          '''bar''': 3.14,
          '''baz''': '''42''',
          '''flag''': True,
      }
      with tempfile.TemporaryDirectory() as tmp_dir:
           _a           =    os.path.join(A					,  '''temp_json'''			)
           os.mkdir(A			)
           with open(temp_local_path + '''.json'''					,  '''w+'''			) as f:
                json.dump(A					,  A			)
           _a           =    parser.parse_yaml_file(Path(temp_local_path + '''.json'''			)			)[0]

      _a           =    BasicExample(**A			)
      self.assertEqual(A					,  A			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)

      _a           =    {
          '''foo''': 12,
          '''bar''': 3.14,
          '''baz''': '''42''',
          '''flag''': True,
      }
      with tempfile.TemporaryDirectory() as tmp_dir:
           _a           =    os.path.join(A					,  '''temp_yaml'''			)
           os.mkdir(A			)
           with open(temp_local_path + '''.yaml'''					,  '''w+'''			) as f:
                yaml.dump(A					,  A			)
           _a           =    parser.parse_yaml_file(Path(temp_local_path + '''.yaml'''			)			)[0]
      _a           =    BasicExample(**A			)
      self.assertEqual(A					,  A			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    HfArgumentParser(A			)
      self.assertIsNotNone(A			)









"""simple docstring"""




from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class        __magic_name__				(			UpperCAmelCase__		,   UpperCAmelCase__		,   UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]





				@register_to_config
				def __init__(			self						,					_a						,					_a						,					_a = None						,					_a = 50_257						,					_a = 1_024						,					_a = 768						,					_a = 12						,					_a = 12						,					_a = None						,					_a = "gelu_new"						,					_a = 0.1						,					_a = 0.1						,					_a = 0.1						,					_a = 1e-5						,					_a = 0.02						,					_a = True						,					_a = True						,					_a = False						,					_a = False						,					):





							"""simple docstring"""
							super().__init__()

							lowerCamelCase      =   prefix_length

							if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
										raise ValueError(
										    f'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
										    f' `n_embd`: {n_embd} are not equal.'					)

							lowerCamelCase      =   prefix_inner_dim
							lowerCamelCase      =   prefix_hidden_dim

							lowerCamelCase      =   (
							    nn.Linear(self.prefix_inner_dim						,					self.prefix_hidden_dim					)
							    if self.prefix_hidden_dim is not None
							    else nn.Identity()
							)
							lowerCamelCase      =   (
							    nn.Linear(self.prefix_hidden_dim						,					_a					) if self.prefix_hidden_dim is not None else nn.Identity()
							)

							lowerCamelCase      =   GPTaConfig(
							    vocab_size=_a						,					n_positions=_a						,					n_embd=_a						,					n_layer=_a						,					n_head=_a						,					n_inner=_a						,					activation_function=_a						,					resid_pdrop=_a						,					embd_pdrop=_a						,					attn_pdrop=_a						,					layer_norm_epsilon=_a						,					initializer_range=_a						,					scale_attn_weights=_a						,					use_cache=_a						,					scale_attn_by_inverse_layer_idx=_a						,					reorder_and_upcast_attn=_a						,					)
							lowerCamelCase      =   GPTaLMHeadModel(_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a = None						,					_a = None						,					):





							"""simple docstring"""
							lowerCamelCase      =   self.transformer.transformer.wte(_a					)
							lowerCamelCase      =   self.encode_prefix(_a					)
							lowerCamelCase      =   self.decode_prefix(_a					)
							lowerCamelCase      =   torch.cat((prefix_embeds, embedding_text)						,					dim=1					)

							if labels is not None:
										lowerCamelCase      =   self.get_dummy_token(input_ids.shape[0]						,					input_ids.device					)
										lowerCamelCase      =   torch.cat((dummy_token, input_ids)						,					dim=1					)
							lowerCamelCase      =   self.transformer(inputs_embeds=_a						,					labels=_a						,					attention_mask=_a					)
							if self.prefix_hidden_dim is not None:
										return out, hidden
							else:
										return out





				def   _lowerCAmelCase							(			self						,					_a						,					_a					):





							"""simple docstring"""
							return torch.zeros(_a						,					self.prefix_length						,					dtype=torch.intaa						,					device=_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.encode_prefix(_a					)





				@torch.no_grad()
				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   torch.split(_a						,					1						,					dim=0					)
							lowerCamelCase      =   []
							lowerCamelCase      =   []
							for feature in features:
										lowerCamelCase      =   self.decode_prefix(feature.to(_a					)					)  # back to the clip feature
										# Only support beam search for now
										lowerCamelCase ,  lowerCamelCase      =   self.generate_beam(
										    input_embeds=_a						,					device=_a						,					eos_token_id=_a					)
										generated_tokens.append(output_tokens[0]					)
										generated_seq_lengths.append(seq_lengths[0]					)
							lowerCamelCase      =   torch.stack(_a					)
							lowerCamelCase      =   torch.stack(_a					)
							return generated_tokens, generated_seq_lengths





				@torch.no_grad()
				def   _lowerCAmelCase							(			self						,					_a=None						,					_a=None						,					_a=None						,					_a = 5						,					_a = 67						,					_a = 1.0						,					_a = None						,					):





							"""simple docstring"""
							lowerCamelCase      =   eos_token_id
							lowerCamelCase      =   None
							lowerCamelCase      =   None
							lowerCamelCase      =   torch.ones(_a						,					device=_a						,					dtype=torch.int					)
							lowerCamelCase      =   torch.zeros(_a						,					device=_a						,					dtype=torch.bool					)

							if input_embeds is not None:
										lowerCamelCase      =   input_embeds
							else:
										lowerCamelCase      =   self.transformer.transformer.wte(_a					)

							for i in range(_a					):
										lowerCamelCase      =   self.transformer(inputs_embeds=_a					)
										lowerCamelCase      =   outputs.logits
										lowerCamelCase      =   logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
										lowerCamelCase      =   logits.softmax(-1					).log()

										if scores is None:
													lowerCamelCase ,  lowerCamelCase      =   logits.topk(_a						,					-1					)
													lowerCamelCase      =   generated.expand(_a						,					*generated.shape[1:]					)
													lowerCamelCase ,  lowerCamelCase      =   next_tokens.permute(1						,					0					), scores.squeeze(0					)
													if tokens is None:
																lowerCamelCase      =   next_tokens
													else:
																lowerCamelCase      =   tokens.expand(_a						,					*tokens.shape[1:]					)
																lowerCamelCase      =   torch.cat((tokens, next_tokens)						,					dim=1					)
										else:
													lowerCamelCase      =   -float(np.inf					)
													lowerCamelCase      =   0
													lowerCamelCase      =   scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													lowerCamelCase      =   scores_sum / seq_lengths[:, None]
													lowerCamelCase ,  lowerCamelCase      =   scores_sum_average.view(-1					).topk(_a						,					-1					)
													lowerCamelCase      =   next_tokens // scores_sum.shape[1]
													lowerCamelCase      =   seq_lengths[next_tokens_source]
													lowerCamelCase      =   next_tokens % scores_sum.shape[1]
													lowerCamelCase      =   next_tokens.unsqueeze(1					)
													lowerCamelCase      =   tokens[next_tokens_source]
													lowerCamelCase      =   torch.cat((tokens, next_tokens)						,					dim=1					)
													lowerCamelCase      =   generated[next_tokens_source]
													lowerCamelCase      =   scores_sum_average * seq_lengths
													lowerCamelCase      =   is_stopped[next_tokens_source]

										lowerCamelCase      =   self.transformer.transformer.wte(next_tokens.squeeze()					).view(generated.shape[0]						,					1						,					-1					)
										lowerCamelCase      =   torch.cat((generated, next_token_embed)						,					dim=1					)
										lowerCamelCase      =   is_stopped + next_tokens.eq(_a					).squeeze()
										if is_stopped.all():
													break

							lowerCamelCase      =   scores / seq_lengths
							lowerCamelCase      =   scores.argsort(descending=_a					)
							# tokens tensors are already padded to max_seq_length
							lowerCamelCase      =   [tokens[i] for i in order]
							lowerCamelCase      =   torch.stack(_a						,					dim=0					)
							lowerCamelCase      =   torch.tensor([seq_lengths[i] for i in order]						,					dtype=seq_lengths.dtype					)
							return output_texts, seq_lengths



